Testing the accuracy of population viability analysis

Despite the growing use of population viability analysis (PTA), the predict ions of these models rarely have been tested with field data that were not used In initially developing the model, We review and discuss a suite of me thods that may be used to test the predictive ability of models used in PVA . In addition to testing mean predictions, appropriate methods must analyze the probability distribution of the model predictions. The methods we disc uss provide tests of the mean predictions, the predicted frequency, of even ts such cis extinction and colonization, and the predicted probability dist ribution of state variables. We discuss visual approaches based on plots of observations versus the predictions and statistical approaches based on de termining significant differences between observations and predictions. The advantages and disadvantages of each method are identified. The best metho ds test the statistical distribution of the predictions; those that ignore variability are meaningless. Although ive recognize that the quality of a m odel is not solely a function of its predictive abilities, tests help reduc e inherent model uncertainty. The role of model testing is not to prove the truth of a model, which is impossible because models are never a perfect d escription of reality. Rather. testing should help identify the weakest asp ects of models so they can be improved. We provide a framework for using mo del testing to improve the predictive performance of PVA models, through an iterative process of model development, testing, subsequent modification a nd re-testing.